6 ways to increase your profit in Retail Marketing using Data Science

About blog:

Welcome readers, in my blog we have discussing the recent trends in the Data Science domain, in my previous post we discussed how this data science domain helpful to agriculture area to get the maximum profit by making use of advanced technology like sensors, data servers and other devices.

So in this article we will discuss the application of Data Science for the Retail market to increase the profit, attract the right customer, campaign the specified group, target the audience etc. Data science reduces our work to find out the right clients for our products that we are selling. Data science do smart work and provide us analytical information as per our requirement which can be used to take further decisions.

You all are aware that Data Science put impact on all type of businesses, industries, including retail markets. According to survey, more that 60% of retailer markets owners make use of Big Data techniques gives them competitive information as well as way to survive with competitor. This data science gives knowledge about your customer what they wants and when they want the product. I presented some necessary methods in this based on applications of data science in the field of retail market.

1.       1. Collaborative filtering:

                Collaborative filtering is a technique having two types, one is based on customer and another in based on product. This technique used to find similar customers or products and multiple ways to calculate rating based on ratings of similar customers. This ratings taken from the customers at the time of feedback after purchasing the product. As per the requirement of retail market owner he can apply filter to find the best customers as well as product which is having more demand.

Price Optimization Systems are mathematical programs that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices that will improve profits.

2. Analyse the Price using optimization technique:

Price optimization techniques can be helpful to retailer for evaluate the potential impact of sales promotions and estimate the right price for each product if they want to sell it in a certain period of time.

Price optimization allows retailers to consider different factors such as:

·         Environmental Conditions and seasons

·         Festivals

·         Special events

·         Customer Demands/requirements

 

3. Cluster Analysis:

Cluster Analysis means that to find out the group of similar kind of objects but they are different from the objects in another group. Lots of data were stored in the database, which needs to sort as per the requirement. We can create groups according to customer, location, product etc. for deciding offers to attract the customer.

 

4. RFM Analysis:

RFM means (Recency, Frequency, Monetary) analysis is a marketing technique used to determine which customers are the best ones by examining how recently a customer has purchased , how often they purchase, and how much the customer spends.

 

5. Propensity Model

A propensity model is a statistical scorecard that is used to predict the behavior of your customer. Propensity models are often used to identify those most likely to respond to an offer, or to focus retention activity on those most likely to churn.

 

6. Cross selling and Up Selling:

Market basket analysis is a data mining technique used by retailers to increase sales by better understanding customer purchasing patterns. It involves analyzing large data sets, such as purchase history, to reveal product groupings, as well as products that are likely to be purchased together.

Conclusion:

Currently data science domain used everywhere for analyzing the customer, product, market value and so many things. 

 

Keep visiting my blog to read such an interesting topics and new technology. In my next article I am going to discuss the upcoming technology called as DevOps.

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